Bibliographic record
Abstract
Dans le livre de Ben Sira, deux textes portent sur la dangereuse beauté des femmes (25,21 et 9,8). Dans cet article, l’auteur cherche à répondre aux questions suivantes. Que faut-il comprendre par « beauté des femmes »? Pourquoi la beauté des femmes est-elle dangereuse? De quelle manière les discours de Ben Sira sur la beauté des femmes sont-ils susceptibles d’influer sur les rapports de pouvoir entre les sexes? Les avertissements au sujet de la beauté, tels qu’on peut les lire dans le texte hébraïque de Ben Sira, correspondent-ils en tout point aux avertissements de son petit-fils, qui a traduit en grec le livre de son grand-père? Que nous disent les divergences sur la manière dont ces deux hommes considèrent la dangerosité de la beauté féminine? Enfin, ces avertissements sont-ils simplement conformes à la mentalité de leur temps ou sont-ils originaux?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".